In order to observe the morphology of a sample such as a biological tissue and simultaneously measure the distribution of the molecules existing in a specified area on the sample, apparatuses called a mass microscope or an imaging mass spectrometer have been developed (refer to: JP-A 2007-66533; JP-A 2007-157353; JP-A 2007-257851; Kiyoshi Ogawa et al., “Kenbi Shitsuryo Bunseki Sochi no Kaihatsu,” (“Research and Development of Mass Microscope”) Shimadzu Review, Shimadzu Corporation, Mar. 31, 2006, vol. 62, nos. 3-4, pp. 125-135; Takahiro Harada et al., “Kenbi Shitsuryo Bunseki Sochi ni yoru Seitai Soshiki Bunseki,” (“Biological Tissue Analysis using Mass Microscope”) Shimadzu Review, Shimadzu Corporation, Apr. 24, 2008, vol. 64, nos. 3-4, pp. 139-146; and other documents). These apparatuses require no grinding or crushing of the sample and hence are capable of obtaining a distribution image (or mapping image) of the ions having a specific mass-to-charge ratio m/z included in any area on the sample specified based on a microscopic observation can be obtained while almost completely maintaining the original morphology of the sample. These apparatuses are expected to be used, for example, to obtain distribution information of the proteins included in a living cell, particularly in the fields of biochemistry, medical care, or pharmaceutical chemistry, and other fields.
It is important for an analysis operator to easily grasp desired information on a sample, e.g. the kind of the substance that characterizes the sample or the distribution of the amount of that substance. To this end, an appropriate analysis processing should be performed to the collected mass spectrometric imaging data and the result of the processing should be displayed in an appropriate form. If mass spectrometric imaging data are obtained for a two-dimensional area of a certain area on a sample, the data will include mass spectrum data of many measurement points (micro areas). Naturally, the amount of these data is enormous. Given this factor, a variety of methods have been proposed to handle such an enormous amount of data and extract meaningful information in an easy-to-understand fashion for the analysis operator.
In one method, for example, an integrated mass spectrum which is obtained by integrating the mass spectra of all measurement points is displayed on a display window. After the analysis operator selects an appropriate peak among the peaks appearing on the integrated mass spectrum, the intensity spatial distribution of the selected peak is displayed by using a commonly available MS image display software product, such as BioMap (for example, refer to “MS Imaging Gijutsu niyoru Byori Soshiki Seppen jou ni okeru Biomarker no Tansaku,” (“Search for Biomarkers on Pathological Samples using MS Imaging Technology”) which is described on Shimadzu Corporation's website). FIG. 6(a) shows examples of the spatial distribution of the peak intensity for different mass-to-charge ratios obtained by this method, and FIG. 6(b) shows an example of a superimposed image of these spatial distributions. Superimposing the spatial distributions of the intensity of two or more peaks in this manner provides information relating to the structure of a specified tissue and the mass-to-charge ratio of the main substance of the tissue.
In another method, a multivariate analysis is used, such as a principal component analysis (PCA), an independent component analysis (ICA), a factor analysis (FA), and other analysis (refer to Morinaga et al., “Development of the software using Principal Component Analysis for MS Imaging Data,” Abstract of the 57th Annual Conference on Mass Spectrometry 2009, Journal of Spectrometry Society of Japan, May 1, 2009 and other documents). In a multivariate analysis, two or more substances forming close intensity spatial distributions gather by factors. Generally, a score and a loading are displayed in terms of each of the factors. In the method described by Morinaga et al., the score is displayed as a two-dimensional spatial distribution, and the loading as a scatter diagram.
However, the previously described conventional methods have the following disadvantages:
In an analysis method using MS image display software, when an analysis operator selects a peak on an integrated mass spectrum, the intensity spatial distribution for a mass-to-charge ratio corresponding to the selected peak is displayed. This method does not guarantee that the selected peak always corresponds to a substance that shows a spatially specific distribution. If a peak showing a spatially specific distribution must be located for each micro area on a sample, the analysis operator needs to compare and superimpose the intensity spatial distributions of two or more peaks by trial and error. Consequently, the operator generally has to repeat the operation of displaying images for many peaks on the integrated mass spectrum, which requires a large amount of labor and time.
In a method using a multivariate analysis, specialized knowledge and skills are required in many cases to determine the number of factors and interpret the loading value of each factor. In the case of PCA, a peak having a negative intensity may be included on a displayed mass spectrum of a main component and hence it is sometimes difficult to interpret the physical meaning of the result. Therefore, not everyone can perform the analysis, which makes it difficult to efficiently perform an analysis and enhance the throughput. Another disadvantage of the PCA method exists in that the information obtained by this method is insufficient for determining the spatial distribution or content of a substance since the spatial distribution obtained by PCA shows only one main component while information relating to the substance is reflected on a plurality of main components.